Structure-based Virtual Screening Approach Research Articles

Identification of Small Molecule Inhibitors of Human Cytomegalovirus pUL89 Endonuclease Using Integrated Computational Approaches.

Replication of Human Cytomegalovirus (HCMV) requires the presence of a metal-dependent endonuclease at the C-terminus of pUL89, in order to properly pack and cleave the viral genome. Therefore, pUL89 is an attractive target to design anti-CMV intervention. Herein, we used integrated structure-based and ligand-based virtual screening approaches in combination with MD simulation for the identification of potential metal binding small molecule antagonist of pUL89. In this regard, the essential chemical features needed for the inhibition of pUL89 endonuclease domain were defined and used as a 3D query to search chemical compounds from ZINC and ChEMBL database. Thereafter, the molecular docking and ligand-based shape screening were used to narrow down the compounds based on previously identified pUL89 antagonists. The selected virtual hits were further subjected to MD simulation to determine the intrinsic and ligand-induced flexibility of pUL89. The predicted binding modes showed that the compounds reside well in the binding site of endonuclease domain by chelating with the metal ions and crucial residues. Taken in concert, the in silico investigation led to the identification of potential pUL89 antagonists. This study provided promising starting point for further in vitro and in vivo studies.

Structure-based virtual screening discovers potent and selective adenosine A1 receptor antagonists

Development of subtype-selective leads is essential in drug discovery campaigns targeting G protein-coupled receptors (GPCRs). Herein, a structure-based virtual screening approach to rationally design subtype-selective ligands was applied to the A1 and A2A adenosine receptors (A1R and A2AR). Crystal structures of these closely related subtypes revealed a non-conserved subpocket in the binding sites that could be exploited to identify A1R selective ligands. A library of 4.6 million compounds was screened computationally against both receptors using molecular docking and 20 A1R selective ligands were predicted. Of these, seven antagonized the A1R with micromolar activities and several compounds displayed slight selectivity for this subtype. Twenty-seven analogs of two discovered scaffolds were designed, resulting in antagonists with nanomolar potency and up to 76-fold A1R-selectivity. Our results show the potential of structure-based virtual screening to guide discovery and optimization of subtype-selective ligands, which could facilitate the development of safer drugs.

Virtual screening and molecular dynamics simulation approach for the identification of potential multi-target directed ligands for the treatment of Alzheimer’s disease

Alzheimer’s disease (AD) is a multifactorial neurological disorder characterized by memory loss and cognitive impairment. The currently available single-targeting drugs have miserably failed in the treatment of AD, and multi-target directed ligands (MTDLs) are being explored as an alternative treatment strategy. Cholinesterase and monoamine oxidase enzymes are reported to play a crucial role in the pathology of AD, and multipotent ligands targeting these two enzymes simultaneously are under various phases of design and development. Recent studies have revealed that computational approaches are robust and trusted tools for identifying novel therapeutics. The current research work is focused on the development of potential multi-target directed ligands that simultaneously inhibit acetylcholinesterase (AChE) and monoamine oxidase B (MAO-B) enzymes employing a structure-based virtual screening (SBVS) approach. The ASINEX database was screened after applying pan assay interference and drug-likeness filter to identify novel molecules using three docking precision criteria; High Throughput Virtual Screening (HTVS), Standard Precision (SP), and Extra Precision (XP). Additionally, binding free energy calculations, ADME, and molecular dynamic simulations were employed to get structural insights into the mechanism of protein-ligand binding and pharmacokinetic properties. Three lead molecules viz. AOP19078710, BAS00314308 and BDD26909696 were successfully identified with binding scores of −10.565, −10.543 & −8.066 kcal/mol against AChE and −11.019, −12.357 & −10.068 kcal/mol against MAO-B, better score as compared to the standard inhibitors. In the near future, these molecules will be synthesized and evaluated through in vitro and in vivo assays for their inhibition potential against AChE and MAO-B enzymes.

Structure-based virtual screening and molecular dynamics approaches to identify new inhibitors of Staphylococcus aureus sortase A

Staphylococcus aureus is a prevalent Gram-positive bacteria leading cause of a wide range of human pathologies. Moreover, antibiotic résistance of pathogenesis bacteria is one of the worldwide health problems. In Gram-positive bacteria, the enzyme of SrtA, is responsible for the anchoring of surface-exposed proteins to the cell wall peptidoglycan. Because of its critical role in Gram-positive bacterial pathogenesis, SrtA is an attractive target for anti-virulence during drug development. To date, some SrtA inhibitors have been discovered most of them being derived from flavonoid compounds, like Myricetin. In order to provide potential hit molecules against SrtA for clinical use, we obtained a total of 293 compounds by performing in silico shape-based screening of compound libraries against Myristin as a reference structure. Employing molecular docking and scoring functions, the top 3 compounds Apigenin, Efloxate, and Compound 8261032 were screened by comparing their docking scores with Myricetin. Furthermore, MD simulations and MM-PBSA binding energy calculation studies revealed that only Compound 8261032 strongly binds to the catalytic core of the SrtA enzyme than Myricetin, and stable behavior was consistently observed in the docking complex. Compound 8261032 showed a good number of hydrogen bonds with SrtA and higher MM-PBSA binding energy when compared to all three molecules. Also, it makes strength interactions with Arg139 and His62, which are critical for SrtA biological activity. This study showed that the development of this inhibitor could be a fundamental strategy against resistant bacteria, but further studies in vitro are needed to confirm this claim. Communicated by Ramaswamy H. Sarma

Introduction of novel drug targets against Staphylococcus aureus and proposing putative inhibitors against adenine N1 (m1A22)-tRNA methyltransferase (TrmK) using computer-aided drug discovery.

Nowadays, the emergence of methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant S. aureus (VRSA) strains has dramatically restricted the treatment options against this microorganism. Therefore, in this study, we aimed to discover new drug targets and inhibitors against S. aureus. This study consists of two major sections. In the upstream evaluation, after a comprehensive core-proteome analysis, essential cytoplasmic proteins with no similarity to the human proteome were selected. Then the S. aureus metabolome-specific proteins were selected, and novel drug targets were identified using the DrugBank database. In the downstream analysis, a structure-based virtual screening approach was performed to reveal potential hit compounds against adenine N1 (m1A22)-tRNA methyltransferase (TrmK) using the StreptomeDB library and AutoDock Vina software. The compounds with a binding affinity > -9 kcal/mol were analyzed based on ADMET properties. Finally, the hit compounds were selected based on Lipinski's rule of five (RO5). Three proteins, including glycine glycosyltransferase (FemA), TrmK, and heptaprenyl pyrophosphate synthase subunit A (HepS1), were selected as feasible and promising drug targets based on PDB file availability and their essential role in the survival of the S. aureus. Finally, seven hit compounds, including Nocardioazine_A, Geninthiocin_D, Citreamicin_delta, Quinaldopeptin, Rachelmycin, Di-AFN_A1 and Naphthomycin_K were introduced against the binding cavity of TrmK, as a feasible drug target. The results of this study provided three feasible drug targets against S. aureus. In the following, seven hit compounds were introduced as potential inhibitors of TrmK, and Geninthiocin_D was identified as the most desirable agent. However, in vivo and in vitro investigations are needed to confirm the inhibitory effect of these agents on S. areus.

Target-driven machine learning-enabled virtual screening (TAME-VS) platform for early-stage hit identification.

High-throughput screening (HTS) methods enable the empirical evaluation of a large scale of compounds and can be augmented by virtual screening (VS) techniques to save time and money by using potential active compounds for experimental testing. Structure-based and ligand-based virtual screening approaches have been extensively studied and applied in drug discovery practice with proven outcomes in advancing candidate molecules. However, the experimental data required for VS are expensive, and hit identification in an effective and efficient manner is particularly challenging during early-stage drug discovery for novel protein targets. Herein, we present our TArget-driven Machine learning-Enabled VS (TAME-VS) platform, which leverages existing chemical databases of bioactive molecules to modularly facilitate hit finding. Our methodology enables bespoke hit identification campaigns through a user-defined protein target. The input target ID is used to perform a homology-based target expansion, followed by compound retrieval from a large compilation of molecules with experimentally validated activity. Compounds are subsequently vectorized and adopted for machine learning (ML) model training. These machine learning models are deployed to perform model-based inferential virtual screening, and compounds are nominated based on predicted activity. Our platform was retrospectively validated across ten diverse protein targets and demonstrated clear predictive power. The implemented methodology provides a flexible and efficient approach that is accessible to a wide range of users. The TAME-VS platform is publicly available at https://github.com/bymgood/Target-driven-ML-enabled-VS to facilitate early-stage hit identification.

Discovery of potent inhibitors targeting Glutathione S-transferase of Wuchereria bancrofti: a step toward the development of effective anti-filariasis drugs.

Lymphatic filariasis (LF) is one of the major health problems for the human kind in developing countries including India. LF is caused by three major nematodes namely Wuchereria bancrofti, Brugia malayi, and Brugia timori. The recent statistics of World Health Organization (WHO) showed that 51 million people were affected and 863 million people from 47 countries around worldwide remain threatened by LF. Among them, 90% of the filarial infection was caused by the nematode W. bancrofti. Approved drugs were available for the treatment of LF but many of them developed drug resistance and no longer effective in all stages of the infection. In the current research work, we explored the Glutathione S-transferase (GST) of W. bancrofti, the key enzyme responsible for detoxification that catalyzes the conjugation of reduced GSH (glutathione) to xenobiotic compounds. Initially, we analyzed the stability of the WbGST through 200 ns MD simulation and further structure-based virtual screening approach was applied by targeting the substrate binding site to identify the potential leads from small molecule collection. The in silico ADMET profiles for the top-ranked hits were predicted and the predicted non-toxic lead molecules showed the highest docking score in the range of -12.72 kcal/mol to -11.97 kcal/mol. The cross docking of the identified hits with human GST revealed the potential binding specificity of the hits toward WbGST. Through WbGST-lead complex simulation, the lead molecules were observed to be stable and also intactly bound within the binding site of WbGST. Based on the computational results, the five predicted non-toxic molecules were selected for the in vitro assay. The molecules showed significant percentage of inhibition against the filarial worm Setaria digitata which is the commonly used model organism to evaluate the filarial activity. In addition, the molecules also showed better IC50 than the standard drug ivermectin. The identified lead molecules will lay a significant insight for the development of new drugs with higher specificity and lesser toxicity to control and treat filarial infections.

In Silico Discovery of Small Molecule Modulators Targeting the Achilles' Heel of SARS-CoV-2 Spike Protein.

The spike protein of SARS-CoV-2 has been a promising target for developing vaccines and therapeutics due to its crucial role in the viral entry process. Previously reported cryogenic electron microscopy (cryo-EM) structures have revealed that free fatty acids (FFA) bind with SARS-CoV-2 spike protein, stabilizing its closed conformation and reducing its interaction with the host cell target in vitro. Inspired by these, we utilized a structure-based virtual screening approach against the conserved FFA-binding pocket to identify small molecule modulators of SARS-CoV-2 spike protein, which helped us identify six hits with micromolar binding affinities. Further evaluation of their commercially available and synthesized analogs enabled us to discover a series of compounds with better binding affinities and solubilities. Notably, our identified compounds exhibited similar binding affinities against the spike proteins of the prototypic SARS-CoV-2 and a currently circulating Omicron BA.4 variant. Furthermore, the cryo-EM structure of the compound SPC-14 bound spike revealed that SPC-14 could shift the conformational equilibrium of the spike protein toward the closed conformation, which is human ACE2 (hACE2) inaccessible. Our identified small molecule modulators targeting the conserved FFA-binding pocket could serve as the starting point for the future development of broad-spectrum COVID-19 intervention treatments.

Zingiberene, a non-zinc-binding class I HDAC inhibitor: A novel strategy for the management of neuropathic pain

BackgroundEven though numerous Histone deacetylase inhibitors (HDACi) have been approved for the treatment of different types of cancer, and others are in clinical trials for the treatment of neurodegenerative diseases, the main problem related to the clinical use of available HDACi is their low isoform selectivity which causes undesirable effects and inevitably limits their therapeutic application. Previously, we demonstrated that a standardized Zingiber officinalis Roscoe rhizome extract (ZOE) reduced neuroinflammation through HDAC1 inhibition in a mice model of neuropathy, and this activity was related to terpenes fraction. Hypothesis/PurposeThe aim of this work was to identify the ZOE constituent responsible for the activity on HDAC1 and to study its possible application in trauma-induced neuropathic pain. MethodsThe ability of ZOE and its terpenes fraction (ZTE) to inhibit HDAC and SIRT isoforms activity and protein expression was assessed in vitro. Then, a structure-based virtual screening approach was applied to predict which constituent could be responsible for the activity. In the next step, the activity of selected compound was tested in an in vitro model of neuroinflammation and in an in vivo model of peripheral neuropathy (SNI). ResultsZTE resulted to be more potent than ZOE on HDAC1, 2, and 6 isoforms, while ZOE was more active on HDAC8. Zingiberene (ZNG) was found to be the most promising HDAC1 inhibitor, with an IC50 of 2.3 ± 0.1 µM. A non-zinc-binding mechanism of inhibition was proposed based on molecular docking. Moreover, the oral administration of ZNG reduced thermal hyperalgesia and mechanical allodynia in animals with neuropathy after 60 min from administration, and decreased HDAC-1 levels in the spinal cord microglia. ConclusionWe found a new non-zinc-dependent inhibitor of HDAC class I, with a therapeutic application in trauma-related neuropathic pain forms in which microglia-spinal overexpression of HDAC1 occurs. The non-zinc-binding mechanism has the potential to reduce off target effects, leading to a higher selectivity and better safety profile, compared to other HDAC inhibitors.

Combination of structure-based virtual screening, molecular docking and molecular dynamics approaches for the discovery of anti-prion fibril flavonoids.

Prion diseases are a group of rare neurodegenerative diseases caused by the structural conversion of cellular prion into Scrapie prion resulting aggregated fibrils. Therapy of prion diseases has been developed for several decades, especially drug designs based on the structure of prion monomers. Unfortunately, none of the designed anti-prion drugs function well clinically. To fight against prion fibrils, a drug design based on the precise structure of mammalian prion fibrils is highly required. Fortunately, based on the advantage of newly advanced cryo-electron microscopy (cryo-EM) in the deconvolution of large complexes, three prion fibril structures were resolved in the last 2years. Based on the cryo-EM solved prion fibril structures, we are able to find some molecules fighting against prion fibrils. Quercetin, one flavonoid molecule in the polyphenol group, has been found to disaggregate the prion fibrils in vitro. In this study, we performed the molecular docking and molecular dynamics simulation on quercetin-like molecules possessing pharmacological properties to evaluate the anti-prion ability of tested molecules. As a result, four quercetin-like molecules interact with prion fibril and decrease the β-strand content by converting some β-strands into loop and helical structures to disintegrate the existing fibril structure. The results of this study are significant in the treatment of prion diseases, and the approaches used in this study are applicable to other amyloid diseases.

Diversifying the chloroquinoline scaffold against SARS-COV-2 main protease: Virtual screening approach using cross-docking, sitemap analysis and molecular dynamics simulation

The absence of designated remedies for coronavirus disease 19 (Covid-19) and the lack of treatment protocols drove scientists to propose new small molecules and to attempt to repurpose existing drugs against various targets of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in order to bring forward efficient solutions. The main protease (Mpro) is one of the most promising drug targets due to its crucial role in fighting viral replication. Several antiviral drugs have been used in an attempt to overcome the pandemic, such as hydroxychloroquine (HCQ). Despite its perceived positive outcomes in the beginning of the disease, HCQ was associated with several drawbacks, such as insolubility, toxicity, and cardiac adverse effects. Therefore, in the present study, a structure-based virtual screening approach was performed to identify structurally modified ligands of the chloroquinoline (CQ) scaffold with good solubility, absorption, and permeation aimed at eventually suggesting a more dependable alternative. PDB ID:7BRP Mpro was chosen as the most reliable receptor after cross-docking calculation using 30 crystal structures. Then, a SiteMap analysis was performed and a total of 231,456 structurally modified compounds of the CQ scaffold were suggested. After Lipinski criteria filtration, 64,312 molecules were docked and their MM-GBSA free binding energy were calculated. Next, ADME descriptors were calculated, and 12 molecules with ADME properties better than that of HCQ were identified. The resulting molecules were subjected to molecular dynamics (MD) simulation for 100 ns. The results of the study indicate that 3 molecules (CQ_22; CQ_2 and CQ_5) show better interactions and stability with the Mpro receptor. Binding interaction analysis indicates that GLU143, THR26, and HIS41 amino acids are potential binding hot-spot residues for the remaining 3 ligands.

-Based Virtual Screening Towards The Discovery of Usnic Acid Derivatives as Novel mTOR Inhibitor to Treat Breast Cancer

In this study, mTOR is chosen as the main target for breast cancer treatment. While the existing drugs still pose severe side effects, research on finding new anti-cancer drug should be done continuously. Usnic acid (UA) has been studied for its wide range of biological properties and potential in pharmaceutical research. A structure-based virtual screening approach is applied since it could reduce production time, cost and environmental issues. This study comprises molecular docking simulation, ADMET filtration and drug-likeness prediction. 340 UA derivatives were retrieved from literature and used to build an in-house database. The resulting compounds from docking were then filtered using ADMET prediction which comprises human intestine absorption, aqueous solubility, plasma protein binding, cytochrome P450 2D6 (CYP2D6) and hepatotoxicity parameters to identify the most potent UA derivatives with favourable physicochemical characteristics. After all, the hit compound, 118, was further stimulated in order to forecast its drug-like features. The chalcone-based scaffold of 118 resembled the reported breast cancer compound’s chemical structure strengthening the results obtained from this study. Thus, it is concluded that the structure-based virtual screening was an efficient and effective approach in the discovery of UA derivative, 118, as a potential novel mTOR inhibitor to treat breast cancer.

Correction: Tabrez et al. Targeting Glutaminase by Natural Compounds: Structure-Based Virtual Screening and Molecular Dynamics Simulation Approach to Suppress Cancer Progression. Molecules 2022, 27, 5042

Due to miscommunication, in the original publication there is a discrepancy in the project number and funding institution, located in Funding Information and Acknowledgement [...].

Insilico exploration of the potential inhibitory activity of DrugBank compounds against CDK7 kinase using structure-based virtual screening, molecular docking, and dynamics simulation approach

The CDK-activating complex (CAK), which includes CDK7, cyclin H, and the RING-finger protein (MAT1), drives cell cycle advancement via T-loop phosphorylation of cell cycle CDKs.The heterotrimeric CAK complex is a component of TFIIH, a generic transcription factor with dual functions in transcription and cell cycle control. CDK7 facilitates transcription by phosphorylating RNA polymerase II (Pol II) at active gene promoters. The “hallmark of cancer” has been attributed to cell cycle dysregulation, as well as aberrant transcriptions mediated by various pathways found in a variety of malignancies. Furthermore, clinical outcomes show that CDK7 levels are abundantly produced in many types of malignancies, implying that it may play a role in tissue maintenance. As a result, CDK7 is regarded as a malignant therapeutic target. Selective CDK7 inhibitors (CDK7i) have been found to work as anti-cancer medications. Drugs being repurposed for CDK7 kinase treatments is a viable strategy to swiftly uncover powerful therapeutic options for some of the most challenging forms of cancer. All of the DrugBank database chemicals, as well as the CDK7 kinase protein, were prepared, and Maestro (Schrödinger Suite) and GROMACS software suite were used to perform Docking, ADMET, MMGBSA, and MD simulation analyses. After screening the DrugBank molecules against CDK7 kinase, compounds including DB07075, DB07163, DB07025, DB01204, DB03916, DB02943, DB07812, and DB07959 were discovered to fit in the active site of the CDK7 kinase and demonstrate tight interactions. The top three docked compounds were tested, and the MD simulation revealed that they were stable with the target protein at 200 ns. As a result, these chemicals have the potential to be effective CDK7 Kinase inhibitors. As a final result, we present DB07075 (3-(5-[4-(aminomethyl)piperidin-1-yl]methyl-1H-indol-2-yl)-1H-indazole-6-carbonitrile) is a reversible inhibitor because it inactivates an enzyme through non-covalent, reversible interactions that could be a more promising inhibitor of CDK7 kinase by interacting with CDK7 kinase. This novel molecule, DB07075 has met all in silico criteria, necessitating further in vitro and in vivo research, especially in clinical trials.

Repositioning of anti-dengue compounds against SARS-CoV-2 as viral polyprotein processing inhibitor.

A therapy for COVID-19 (Coronavirus Disease 19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) remains elusive due to the lack of an effective antiviral therapeutic molecule. The SARS-CoV-2 main protease (Mpro), which plays a vital role in the viral life cycle, is one of the most studied and validated drug targets. In Several prior studies, numerous possible chemical entities were proposed as potential Mpro inhibitors; however, most failed at various stages of drug discovery. Repositioning of existing antiviral compounds accelerates the discovery and development of potent therapeutic molecules. Hence, this study examines the applicability of anti-dengue compounds against the substrate binding site of Mpro for disrupting its polyprotein processing mechanism. An in-silico structure-based virtual screening approach is applied to screen 330 experimentally validated anti-dengue compounds to determine their affinity to the substrate binding site of Mpro. This study identified the top five compounds (CHEMBL1940602, CHEMBL2036486, CHEMBL3628485, CHEMBL200972, CHEMBL2036488) that showed a high affinity to Mpro with a docking score > -10.0 kcal/mol. The best-docked pose of these compounds with Mpro was subjected to 100 ns molecular dynamic (MD) simulation followed by MM/GBSA binding energy. This showed the maximum stability and comparable ΔG binding energy against the reference compound (X77 inhibitor). Overall, we repurposed the reported anti-dengue compounds against SARS-CoV-2-Mpro to impede its polyprotein processing for inhibiting SARS-CoV-2 infection.

Structure-based virtual screening and molecular dynamics of potential inhibitors targeting sodium-bile acid co-transporter of carcinogenic liver fluke Clonorchis sinensis.

Clonorchis sinensis requires bile acid transporters as this fluke inhabits bile juice-filled biliary ducts, which provide an extreme environment. Clonorchis sinensis sodium-bile acid co-transporter (CsSBAT) is indispensable for the fluke's survival in the final host, as it circulates taurocholate and prevents bile toxicity in the fluke; hence, it is recognized as a useful drug target. In the present study, using structure-based virtual screening approach, we presented inhibitor candidates targeting a bile acid-binding pocket of CsSBAT. CsSBAT models were built using tertiary structure modeling based on a bile acid transporter template (PDB ID: 3zuy and 4n7x) and were applied into AutoDock Vina for competitive docking simulation. First, potential compounds were identified from PubChem (holding more than 100,000 compounds) by applying three criteria: i) interacting more favorably with CsSBAT than with a human homolog, ii) intimate interaction to the inward- and outward-facing conformational states, iii) binding with CsSBAT preferably to natural bile acids. Second, two compounds were identified following the Lipinski's rule of five. Third, other two compounds of molecular weight higher than 500 Da (Mr > 500 Da) were presumed to efficiently block the transporter via a feasible rational screening strategy. Of these candidates, compound 9806452 exhibited the least hepatotoxicity that may enhance drug-likeness properties. It is proposed that compound 9806452 act as a potential inhibitor toward CsSBAT and further studies are warranted for drug development process against clonorchiasis.

Combination of ligand and structure based virtual screening approaches for the discovery of potential PARP1 inhibitors.

Poly (ADP-ribose) polymerase 1 (PARP1) has high therapeutic value as biomolecular target for research and development of small molecules with antineoplastic activity, since it is upregulated in many cancers, especially in ovarian and BRCA 1/2 mutated breast cancers. Decades of investigation of PARP inhibitors (PARPi) have led to the approval of several drug compounds, however clinical application of PARPi in cancer therapy is limited due to a number of factors, including low selectivity, weak affinity and undesired side effects. Thus, identification of novel drug-like chemical compounds with alternatives to the known PARPi chemical scaffolds, binding modes and interaction patterns with amino acid residues in the active site is of high therapeutic importance. In this study we applied a combination of ligand- and structure-based virtual screening approaches with the goal of identification of novel potential PARPi.

Machine Learning Enabled Structure-Based Drug Repurposing Approach to Identify Potential CYP1B1 Inhibitors.

Drug-metabolizing enzyme (DME)-mediated pharmacokinetic resistance of some clinically approved anticancer agents is one of the main reasons for cancer treatment failure. In particular, some commonly used anticancer medicines, including docetaxel, tamoxifen, imatinib, cisplatin, and paclitaxel, are inactivated by CYP1B1. Currently, no approved drugs are available to treat this CYP1B1-mediated inactivation, making the pharmaceutical industries strive to discover new anticancer agents. Because of the extreme complexity and high risk in drug discovery and development, it is worthwhile to come up with a drug repurposing strategy that may solve the resistance problem of existing chemotherapeutics. Therefore, in the current study, a drug repurposing strategy was implemented to find the possible CYP1B1 inhibitors using machine learning (ML) and structure-based virtual screening (SB-VS) approaches. Initially, three different ML models were developed such as support vector machines (SVMs), random forest (RF), and artificial neural network (ANN); subsequently, the best-selected ML model was employed for virtual screening of the selleckchem database to identify potential CYP1B1 inhibitors. The inhibition potency of the obtained hits was judged by analyzing the crucial active site amino acid interactions against CYP1B1. After a thorough assessment of docking scores, binding affinities, as well as binding modes, four compounds were selected and further subjected to in vitro analysis. From the in vitro analysis, it was observed that chlorprothixene, nadifloxacin, and ticagrelor showed promising inhibitory activity toward CYP1B1 in the IC50 range of 0.07–3.00 μM. These new chemical scaffolds can be explored as adjuvant therapies to address CYP1B1-mediated drug-resistance problems.

Discovery of Epipodophyllotoxin-Derived B2 as Promising XooFtsZ Inhibitor for Controlling Bacterial Cell Division: Structure-Based Virtual Screening, Synthesis, and SAR Study.

The emergence of phytopathogenic bacteria resistant to antibacterial agents has rendered previously manageable plant diseases intractable, highlighting the need for safe and environmentally responsible agrochemicals. Inhibition of bacterial cell division by targeting bacterial cell division protein FtsZ has been proposed as a promising strategy for developing novel antibacterial agents. We previously identified 4′-demethylepipodophyllotoxin (DMEP), a naturally occurring substance isolated from the barberry species Dysosma versipellis, as a novel chemical scaffold for the development of inhibitors of FtsZ from the rice blight pathogen Xanthomonas oryzae pv. oryzae (Xoo). Therefore, constructing structure−activity relationship (SAR) studies of DMEP is indispensable for new agrochemical discovery. In this study, we performed a structure−activity relationship (SAR) study of DMEP derivatives as potential XooFtsZ inhibitors through introducing the structure-based virtual screening (SBVS) approach and various biochemical methods. Notably, prepared compound B2, a 4′-acyloxy DMEP analog, had a 50% inhibitory concentration of 159.4 µM for inhibition of recombinant XooFtsZ GTPase, which was lower than that of the parent DMEP (278.0 µM). Compound B2 potently inhibited Xoo growth in vitro (minimum inhibitory concentration 153 mg L−1) and had 54.9% and 48.4% curative and protective control efficiencies against rice blight in vivo. Moreover, compound B2 also showed low toxicity for non-target organisms, including rice plant and mammalian cell. Given these interesting results, we provide a novel strategy to discover and optimize promising bactericidal compounds for the management of plant bacterial diseases.

Targeting Glutaminase by Natural Compounds: Structure-Based Virtual Screening and Molecular Dynamics Simulation Approach to Suppress Cancer Progression.

Cancer cells change their glucose and glutamine (GLU) metabolism to obtain the energy required to continue growing. Glutaminase (GLS) plays a crucial role in promoting cell metabolism for cancer cell growth; targeting GLU metabolism by inhibiting GLS has attracted interest as a potential cancer management strategy. Herein, we employed a sequential screening of traditional Chinese medicine (TCM) database followed by drug-likeness and molecular dynamics simulations against the active site of GLS. We report 12 potent compounds after screening the TCM database against GLS, followed by a drug-likeness filter with Lipinski and Veber rule criteria. Among them, ZINC03978829 and ZINC32296657 were found to have higher binding energy (BE) values than the control compound 6-Diazo-5-Oxo-L-Norleucine, with BEs of −9.3 and −9.7 kcal/mol, respectively, compared to the BE of 6-Diazo-5-Oxo-L-Norleucine (−4.7 kcal/mol) with GLS. Molecular dynamics simulations were used to evaluate the results further, and a 100 ns MD simulation revealed that the hits form stable complexes with GLS and formed 2–5 hydrogen bond interactions. This study indicates that these hits might be employed as GLS inhibitors in the battle against cancer. However, more laboratory tests are a prerequisite to optimize them as GLS inhibitors.